






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFXLMRobertaModel




@require_tf
@require_sentencepiece
@require_tokenizers
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			TFXLMRobertaModel.from_pretrained('jplu/tf-xlm-roberta-base'	)

         a_						     =			{
             'input_ids': tf.convert_to_tensor([[0, 2_646, 10_269, 83, 99_942, 2]]   ,	dtype=tf.intaa	),  # "My dog is cute"
             'attention_mask': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]   ,	dtype=tf.intaa	),
         }

         a_						     =			model(UpperCamelCase__	)['last_hidden_state']
         a_						     =			tf.TensorShape((1, 6, 768)	)
         self.assertEqual(output.shape   ,	UpperCamelCase__	)
         # compare the actual values for a slice.
         a_						     =			tf.convert_to_tensor(
             [
                 [
                     [0.0_681_762, 0.10_894_451, 0.06_772_504],
                     [-0.06_423_668, 0.02_366_615, 0.04_329_344],
                     [-0.06_057_295, 0.09_974_135, -0.00_070_584],
                 ]
             ]   ,	dtype=tf.floataa   ,	)

         self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,	expected_slice.numpy()   ,	atol=1e-4	)	)












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




